Task Scheduling on the Cloud with Hard Constraints

TL;DR

This paper presents a heuristic algorithm for scheduling Bag-of-Tasks applications on the cloud, optimizing for hard constraints of budget and deadline, and demonstrates its effectiveness through scenario-based evaluations.

Contribution

It introduces a novel heuristic algorithm that effectively manages hard budget and deadline constraints for cloud-based BoT scheduling, considering multiple resource types.

Findings

The algorithm successfully satisfies user-defined budget and deadline constraints.

Using multiple resource types can outperform single resource type strategies.

Experimental results validate the algorithm's feasibility and efficiency.

Abstract

Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more challenging than grid and cluster environ- ments. This is because a user may have a budgetary constraint or a deadline for executing the BoT application in order to keep the overall execution costs low. The research in this paper is motivated to investigate task scheduling on the cloud, given two hard constraints based on a user-defined budget and a deadline. A heuristic algorithm is proposed and implemented to satisfy the hard constraints for executing the BoT application in a cost effective manner. The proposed algorithm is evaluated using four scenarios that are based on the trade-off between performance and the cost of using different cloud resource types. The experimental evaluation confirms the feasibility of the algorithm in satisfying the constraints. The key observation is that multiple resource types can be a better alternative to using a single type of resource.